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Under the classical mean-variance framework, the purpose of this paper is to investigate the properties of the instability of minimal variance portfolio and then propose a novel penalized expected risk criterion (PERC) for optimal portfolio selection.

The proposed method considers not only a portfolio’s expected risk, but also its instability that is quantified by the variance of the estimated portfolio weights. This study tests the out-of-sample performance of various portfolio selection methods on both China and US stock markets.

It is very useful to control portfolio stability in real application of portfolio selection. The empirical results on both US and China stock markets show that PERC portfolio effectively controls turnover and consequently the transaction cost, and that is why it is so competing compared with other alternative methods.

The findings suggest that the rebalancing turnover and the associated transaction cost that is usually ignored in theoretical analysis play a very important role in real investment.

For investors, especially institutional investors, the rebalancing turnover and corresponding transaction cost must be carefully addressed. The variance of the estimated portfolio weights is a good candidate to quantify portfolio instability.

This study addresses the important role of portfolio instability and proposes a novel expected risk criterion for portfolio selection after the quantitative definition of portfolio instability.

This paper aims to provide an overview of the main research topics in the emerging fields of cyber risk and cyber risk insurance. The paper also illustrates future research directions, from both academic and practical points of view.

The authors conduct a literature review on cyber risk and cyber risk insurance using a standardized search and identification process that has been used in various academic articles. Based upon this selection process, a database of 209 papers is created. The main research results findings are extracted and organized in seven clusters.

The results illustrate the immense difficulties to insure cyber risk, especially due to a lack of data and modelling approaches, the risk of change and incalculable accumulation risks. The authors discuss various ways to overcome these insurability limitations, such as mandatory reporting requirements, pooling of data or public–private partnerships in which the government covers parts of the risk.

Despite its increasing relevance for businesses at present, research on cyber risk is limited. Many papers can be found in the IT domain, but relatively little research has been done in the business and economics literature. The authors illustrate where research stands currently and outline directions for future research.

The purpose of this paper is to examine the risk neutral and non-risk neutral pricing of Singapore Real Estate Investment Trusts (S-REITs) via comparing the average of the individual ratios (of deviation between expected and observed closing price/observed closing price) with the ratio (of standard deviation/mean) for closing prices via the binomial options pricing tree model.

If the ratio (of standard deviation/mean) ratio > the ratio (of deviation between expected and observed closing price/observed closing price), then the deviation of closing prices from the expected risk neutral prices is not significant and that the S-REIT is consistent with risk neutral pricing. If the ratio (of deviation between expected and observed closing price/observed closing price) is greater, then the S-REIT is not consistent with risk neutral pricing.

Capitacommercial Trust (CCT), Capitamall Trust (CMT) and Keppel Real Estate Investment Trust (REIT) have large positive differences between the two ratios (39.86, 30.79 and 18.96 percent, respectively), implying that these S-REITs are not trading at risk neutral pricing. Suntec REIT has a small positive difference of 2.35 percent between both ratios, implying that it is trading at risk neutral pricing. Ascendas REIT has the largest negative difference between the two ratios at −4.24 percent, to be followed by Mapletree Logistics Trust at −0.44 percent. Both S-REITs are trading at risk neutral pricing. The analysis shows that CCT, CMT and Keppel REIT exhibit risk averse pricing.

Results are consistent with prudential asset allocation for viable S-REIT portfolio investing but that not all these S-REITs exhibit strong market efficiency in their pricing.

Pricing may be risk neutral over a certain period but investor sentiments, fear of risks and speculative activities could affect an S-REIT’s risk neutrality.

With enhanced risk diversification activities, the S-REITs should attain risk neutral pricing.

Virtually no research of this nature has been undertaken for S-REITS.

The purpose of this paper is to investigate how entrepreneurs demand for external finance changed as the economy continued to be mired in its third and fourth years of the global financial crisis (GFC) and whether or not external finance has become more difficult to access as the recession progressed.

Using a large-scale survey data on over 30,000 UK small- and medium-sized enterprises between July 2011 and March 2013, the authors estimate a series of conditional probit models to empirically test the determinants of the supply of, and demand for external finance.

Older firms and those with a higher risk rating, and a record of financial delinquency, were more likely to have a demand for external finance. The opposite was true for women-led businesses and firms with positive profits. In general finance was more readily available to older firms post-GFC, but banks were very unwilling to advance money to firms with a high-risk rating or a record of any financial delinquency. It is estimated that a maximum of 42,000 smaller firms were denied credit, which was significantly lower than the peak of 119,000 during the financial crisis.

This paper provides timely evidence that adds to the general understanding of what really happens in the market for small business financing three to five years into an economic downturn and in the early post-GFC period, from both a demand and supply perspective. This will enable the authors to consider what the potential impacts of credit rationing on the small business sector are and also identify areas where government action might be appropriate.

The purpose of this paper is to increase the accuracy of the efficient portfolios frontier and the capital market line using the Riskiness Index.

This paper will develop the mean-riskiness model for portfolio selection using the Riskiness Index.

This paper’s main result is establishing a mean-riskiness efficient set of portfolios. In addition, the paper presents two applications for the mean-riskiness portfolio management method: one that is based on the multi-normal distribution (which is identical to the MV model optimal portfolio) and one that is based on the multi-normal inverse Gaussian distribution (which increases the portfolio’s accuracy, as it includes the a-symmetry and tail-heaviness features in addition to the scale and diversification features of the MV model).

The Riskiness Index is not a coherent measurement of financial risk, and the mean-riskiness model application is based on a high-order approximation to the portfolio’s rate of return distribution.

The mean-riskiness model increases portfolio management accuracy using the Riskiness Index. As the approximation order increases, the portfolio’s accuracy increases as well. This result can lead to a more efficient asset allocation in the capital markets.

The purpose of this paper is to focus on examining the first day returns of initial public offerings (IPOs) and the role of information on their performance. The study tries to optimize the returns of the new issues during 2005-2012 with risk as a constraint.

The initial returns are measured through the market-adjusted excess return and the risk associated with the new issue is measured through underwriters’ reputation. The returns have been optimized through a mixed integer linear problem using the Maple software.

The previous studies show that various informational variables affect the listing day returns significantly. The results of the present study indicate that the mean of initial returns for IPOs during 2005-2012 is 18.03 and the mean risk for these issues is 0.46. The findings also suggest that the optimal returns are obtained in the pre-recession era (2005-2008) and the value for the same is 50.02 percent.

The current study contributes in the investment decisions for global investors as every investor wants to maximize his/her returns. The optimal returns with risk as a constraint will help the investors in improving their investment decision as a prudent investor does not aim solely at maximizing the expected return of an investment but is also interested in optimizing with the minimization of risk.

Property and casualty (“P&C”) insurance companies rely on “risk capital” to absorb large losses that unexpectedly deplete claims‐paying resources and reduce underwriting capacity. The purpose of this paper is to review the similarities and differences between two different types of risk capital raised by insurers to cover losses arising from natural catastrophes: internal risk capital provided by investors in insurance company debt and equity; and external risk capital provided by third parties. The paper also explores the distinctions between four types of external catastrophe risk capital: reinsurance, industry loss warranties, catastrophe derivatives, and insurance‐linked securities. Finally, how the credit crisis has impacted alternative sources of catastrophe risk capital in different ways is considered.

The discussion is based on the conceptual framework for analyzing risk capital developed by Merton and Perold.

In 2008, the P&C insurance industry was adversely affected by significant natural catastrophe‐related losses, floundering investments, and limited access to capital markets, all of which put upward pressure on catastrophe reinsurance premiums. But the influx of new risk capital that generally accompanies hardening markets has been slower than usual to occur in the wake of the credit crisis. Meanwhile, disparities between the relative costs and benefits of alternative sources of catastrophe risk capital are even more pronounced than usual.

Although many insurance companies focus on how much reinsurance to buy, this paper emphasizes that a more important question is how much risk capital to acquire from external parties (and in what form) vis‐à‐vis investors in the insurance company's own securities.

This paper aims to study a strategic decision of banks in Thailand to signal their types to the market and derive the optimal credit derivatives contract to guarantee their loans and credibly signal their quality under different economic determinants, namely, the maximum credit risk investment constraint, opportunity cost and opaqueness of the credit derivative market.

Contract theory is deployed to derive the expected payoff of different bank types under different economic and financial constraints. Hence, different bank types offer derivatives contracts to signal their loan quality and resell their loans in the secondary loan markets of Thailand.

The optimal derivatives contract is constructed on a basis of asymmetric information when banks have more private information concerning quality of their loans. A digital credit default swap is an optimal derivatives contract to send credible signal when banks are restricted to the maximum investment constraint. Moreover, profit of banks is reduced, as the optimal derivatives contract is more costly when banks are subjected to positive opportunity cost and opacity of the credit derivatives market. These results depict impact of changes of the maximum credit risk investment constraint on Thai credit derivatives market.

The optimal credit derivatives design that signifies bank types and facilitates loan purchase agreement has not been studied in Thai secondary loan markets before. In addition, this study provides insights of banks' strategic decisions to signal their types and transfer risk to risk buyers in Thai markets.

The heavy civil engineering industry (railways, sewage treatment, chemical and pharmaceutical facilities, oil and gas facilities, etc.) is one of the major contributors to the British economy and generally involves a high level of investment. Clients in this industry are demanding accurate cost estimate, proper analysis of out‐turn cost and cost escalation and a high quality risk analysis throughout the construction processes. Current practice in the industry has suggested that there is a lack of structured methodologies and systematic cost escalation approach to achieve an appropriate cost analysis at the outset of projects and throughout the construction processes. In this context the prime objective of this research work is to develop a structured cost escalation methodology for improving estimating management and control in the heavy engineering industry construction processes. The methodology is composed of a forecasting model to predict cost indices of major items in industry and a risk knowledge base model for identifying and quantifying causes of cost escalations. This paper, as part of the research, reviews and discusses a knowledge‐based model for applying a cost escalation factor. The cost escalation factor is made up of market variation, a risk element and a component for bias. A knowledge elicitation strategy was employed to obtain the required knowledge for the model. The strategy included questionnaires, interviews and workshops and deliverables came in the form of influences and their effect on project cost escalation. From these deliverables, the concepts of a decision support model and system specification for applying cost escalation to base estimates is proposed.

Discusses some of the lessons arising from the work of Sypol Environmental Management in conducting risk assessment and audit exercises at hospital sites and in the community. Points towards the future development of a health and safety culture within health care.